Kubernetes is increasingly the default platform for AI and machine learning workloads. Kubeflow is a popular tool that leverages Kubernetes' Custom Resource Definitions (CRDs) for managing these ML tasks. While Kubeflow's specialized dashboards help data scientists, they often obscure the underlying Kubernetes layer, forcing operators to use kubectl for troubleshooting. The Headlamp Kubeflow plugin bridges this gap by displaying Kubeflow's custom resources directly within a general-purpose Kubernetes UI. This allows operators to observe and manage ML resources using the same primitives as other cluster components. Headlamp itself is an extensible Kubernetes web UI maintained under Kubernetes SIG UI. The plugin provides cluster-level insights by directly querying the Kubernetes API server, offering details on Pod conditions, failure reasons, and resource states across namespaces. It supports various Kubeflow components like Notebooks, Pipelines, Katib for hyperparameter tuning, Training, and Spark. The plugin allows inspection of notebook Pod details, Katib trial statuses, and pipeline states without relying on Kubeflow's backend database. It also maps ML resources as graph nodes within Headlamp, visualizing relationships between them. This approach demonstrates a pattern for any CRD-heavy platform to provide operators with essential cluster-level visibility directly within their familiar tools.